Showing 7 open source projects for "model-builder"

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  • 1
    Real-ESRGAN GUI

    Real-ESRGAN GUI

    Cross-platform GUI for image upscaler Real-ESRGAN

    ...According to actual measurements, arm64the single-architecture performance is better than universal2the dual- architecture Mac on the Apple chip, so Apple chip users are advised to pack arm64single-architecture applications by themselves. Real-ESRGAN can only enlarge the input image with a fixed 2-4x magnification (related to the selected model). This functionality is achieved by downsampling using a conventional scaling algorithm after multiple calls to Real-ESRGAN. Split each frame of the GIF and record the duration, zoom in one by one and then merge. Drag an image file or directory to any position in the window, and its path can be automatically set as the input.
    Downloads: 94 This Week
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  • 2
    AI Models

    AI Models

    A repository of trained models

    All models (at least currently) are supported by chaiNNer, an upscaling GUI that allows for both very simple and very complex tasks to be completed in a nice manner where you "chain" nodes together. Highly recommended for images. If you're looking to upscale videos using the models then use enhancr simply due to the fact that it supports TensorRT, which will allow you to upscale videos at incredible speeds! The GUI is one of the best looking applications out there and is personally my go to...
    Downloads: 3 This Week
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  • 3
    G2SConverter

    G2SConverter

    Convert models from GoldSource engine to Source engine with AI

    ...An example of a processed texture is shown in the following image (parameters used: scaling-factor = 4 and deblur iterations = 4) besides upscaling and debluring the utility also generates normal maps for each texture. This is implemented using the DeepBump by HugoTiny model. Examples of normal maps are shown in the following images.
    Downloads: 0 This Week
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  • 4
    VSGAN

    VSGAN

    VapourSynth Single Image Super-Resolution Generative Adversarial

    Single Image Super-Resolution Generative Adversarial Network (GAN) which uses the VapourSynth processing framework to handle input and output image data. Transform, Filter, or Enhance your input video, or the VSGAN result with VapourSynth, a Script-based NLE. You can chain models or re-run the model twice-over (or more). Have low VRAM? Don’t worry! The Network will be applied in quadrants of the image to reduce up-front VRAM usage. You can use any RGB video input, including float32 (e.g., RGBS) inputs. Using VapourSynth you can pass a Video directly to VSGAN, without any frame extraction needed. Any edit you make in the VapourSynth script with or without VSGAN can be re-used for any other video. ...
    Downloads: 0 This Week
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  • 5
    Easy Upscale

    Easy Upscale

    A simple image upscaler application using EDSR, ESPCN, FSRCNN, etc.

    ...The main theme is queues, we implement circular queues for pooling/storing a list of images to be upscaled. Gui creation is made manually using the tkinter library. For the upscale process itself, it uses the OpenCV library with a model obtained from open source. Checked using vermin. Minimum required versions: 3.6 Incompatible versions: 2.
    Downloads: 0 This Week
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  • 6
    Robust Video Matting (RVM)

    Robust Video Matting (RVM)

    Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX

    We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and can process 4K at 76 FPS and HD at 104 FPS on an Nvidia GTX 1080Ti GPU. Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and...
    Downloads: 10 This Week
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  • 7
    Super-résolution via CNN

    Super-résolution via CNN

    Super resolution using a CNN, based on the work of the DGtal team

    ...First of all, an Nvidia graphics card (neither AMD nor Intel integrated) is highly recommended to parallelize the CNN. You will then need to install CUDA. No CUDA = dozens of times slower. This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test batch and lrto the learning rate. Low res images should be located in a "dataset/input" folder, and high res targets in a "dataset/target" folder, where each different quality image has the same name in both folders.
    Downloads: 0 This Week
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